3 citations · 3 across the 1 of their papers we have counts for
6 papers
Substructure Substitution: Structured Data Augmentation for NLP
Haoyue Shi, Karen Livescu, Kevin Gimpel
We study a family of data augmentation methods, substructure substitution (SUB2), for natural language processing (NLP) tasks. SUB2 generates new examples by substituting substruct…
On the Role of Supervision in Unsupervised Constituency Parsing
Haoyue Shi, Karen Livescu, Kevin Gimpel
We analyze several recent unsupervised constituency parsing models, which are tuned with respect to the parsing score on the Wall Street Journal (WSJ) development set (1,700…
A Cross-Task Analysis of Text Span Representations
Shubham Toshniwal, Haoyue Shi, Bowen Shi +3
Many natural language processing (NLP) tasks involve reasoning with textual spans, including question answering, entity recognition, and coreference resolution. While extensive res…
Visually Grounded Neural Syntax Acquisition
Haoyue Shi, Jiayuan Mao, Kevin Gimpel +1
We present the Visually Grounded Neural Syntax Learner (VG-NSL), an approach for learning syntactic representations and structures without any explicit supervision. The model learn…
On Tree-Based Neural Sentence Modeling
Haoyue Shi, Hao Zhou, Jiaze Chen +1
Neural networks with tree-based sentence encoders have shown better results on many downstream tasks. Most of existing tree-based encoders adopt syntactic parsing trees as the expl…
Learning Visually-Grounded Semantics from Contrastive Adversarial Samples
Haoyue Shi, Jiayuan Mao, Tete Xiao +2
We study the problem of grounding distributional representations of texts on the visual domain, namely visual-semantic embeddings (VSE for short). Begin with an insightful adversar…